The DOI matches its Crossref record.
Statistical Downscaling and Bias Correction for Climate Research
Abstract
Statistical downscaling and bias correction are becoming standard tools in climate impact studies. This book provides a comprehensive reference to widely-used approaches, and additionally covers the relevant user context and technical background, as well as a synthesis and guidelines for practitioners. It presents the main approaches including statistical downscaling, bias correction and weather generators, along with their underlying assumptions, skill and limitations. Relevant background information on user needs and observational and climate model uncertainties is complemented by concise introductions to the most important concepts in statistical and dynamical modelling. A substantial part is dedicated to the evaluation of regional climate projections and their value in different user contexts. Detailed guidelines for the application of downscaling and the use of downscaled information in practice complete the volume. Its modular approach makes the book accessible for developers and practitioners, graduate students and experienced researchers, as well as impact modellers and decision makers.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Machine Learning Methods in Weather and Climate Applications: A Survey
Chen, L., Han, B., Wang, X., Zhao, J., Yang, W., & Yang, Z. (2023) · doi:10.20944/preprints202309.1764.v2 · Cited by 246
High-resolution terrestrial climate, bioclimate and vegetation for the last 120,000 years
Beyer, R. M., Krapp, M., & Manica, A. (2020) · Scientific Data · doi:10.1038/s41597-020-0552-1 · Cited by 110
Schirrmeister, L., Fuchs, M. C., Opel, T., Andreev, A., Kienast, F., Schneider, A., Nazarova, L., Frolova, L., Kuzmina, S., Kuznetsova, T., et al. (2025) · Climate of the Past · doi:10.5194/cp-21-1143-2025 · Cited by 5
Metadata
- Authors
- Maraun, D., & Widmann, M.
- Venue
- Cambridge University Press eBooks
- Publisher
- Cambridge University Press
- Published
- 2017-12-27
- OpenAlex
- W2781302587
- Cited by
- 548
- References
- 0
- FWCI
- 28.86
- Type
- book
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Statistical Downscaling and Bias Correction for Climate Research. https://app.cochat.ai/papers/10.1017/9781107588783
- The paper
- Maraun, D., & Widmann, M. (2018). Statistical Downscaling and Bias Correction for Climate Research. Cambridge University Press eBooks. https://doi.org/10.1017/9781107588783
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
